Journal of Diabetes and Metabolic Disorders最新文献

{"title":"The role of branched chain aminotransferase in the interrelated pathways of type 2 diabetes mellitus and Alzheimer's disease.","authors":"Haya Majid, Sunil Kohli, Sajad Ul Islam, Nidhi","doi":"10.1007/s40200-025-01597-6","DOIUrl":"10.1007/s40200-025-01597-6","url":null,"abstract":"<p><strong>Objectives: </strong>This review assessed the role of Branched-Chain Amino Acid Transaminase (BCAT) enzymes in human metabolism, and their involvement in the catabolism of branched-chain amino acids (BCAAs) and exploring the association between Type 2 Diabetes Mellitus (T2DM) and Alzheimer's disease (AD) through insulin resistance.</p><p><strong>Methods: </strong>The analysis involves a comprehensive literature review of recent research findings related to BCAT enzymes, BCAA metabolism, T2DM, and AD. Relevant studies and articles were identified through systematic searches in databases such as PubMed, ScienceDirect, and other scholarly resources. Inclusion criteria encompassed research articles, reviews, and studies published in peer-reviewed journals, with a focus on human metabolism, BCAT enzymes, and the interplay between BCAA metabolism, T2DM, and AD.</p><p><strong>Results: </strong>The association between T2DM and AD suggests a potential metabolic link, particularly through dysregulated BCAA metabolism leading to insulin resistance. The impact of impaired insulin signaling is implicated in brain function and the accumulation of amyloid plaques facilitated by BCAT.</p><p><strong>Conclusion: </strong>The identified link between BCAT, BCAA metabolism, T2DM, and AD suggests that disruptions in BCAT levels could serve as valuable indicators for early detection of insulin resistance and cognitive impairment as observed in Type 3 Diabetes which may present a promising therapeutic target.</p>","PeriodicalId":15635,"journal":{"name":"Journal of Diabetes and Metabolic Disorders","volume":"24 1","pages":"90"},"PeriodicalIF":1.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11936868/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143730288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The discriminatory ability of wrist and neck circumference in predicting insulin resistance in overweight and obese children.","authors":"Hamid Asayesh, Ali Dehghan, Sahar Sobhani, Fereshteh Bayegi, Sayeh Rostami, Fatemeh Aghamahdi, Mostafa Qorbani","doi":"10.1007/s40200-025-01603-x","DOIUrl":"10.1007/s40200-025-01603-x","url":null,"abstract":"<p><strong>Objectives: </strong>The aim of this study was to investigate the association of wrist circumference (WrC) and neck circumferences (NC) with Insulin Resistance (IR) in obese and overweight children and adolescents.</p><p><strong>Methods: </strong>This cross-sectional study included 227 overweight and obese children. Anthropometric indices such as NC and WrC were measured. Laboratory parameters such as fasting blood glucose (FBS) and insulin were measured after 12 h of overnight fasting. IR was determined by Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) using formula and HOMA-IR ≥ 2.6 defined as IR. The predictive power of NC and WrC for IR was assessed using receiver operating characteristic (ROC) analyses and the area under ROC curve (AUC) > 0.65 were considered as highly accurate tests.</p><p><strong>Results: </strong>Among the 227 included samples, 52.4% were girls, and 67.4% were classified as obese. IR was detected in 48.5% of the participants without a significant association with gender (48.8% in girls and 48.1% in boys) and weight status (43.2% in overweight and 51% in obese). The AUCs of WrC and NC in detecting IR were 0.78 (95% CI: 0.72-0.84) and 0.72 (95% CI: 0.65-0.78) in overweight and obese children respectively. The Chi-square test shows that the AUC of WrC in predicting IR was statistically higher than NC (Chi-square: 4.47, P: 0.03).</p><p><strong>Conclusions: </strong>Our findings showed that WrC and NC are two useful indices for predicting IR in overweight and obese children and adolescents. Therefore they could be used as a clinical indicators of IR in children and adolescents.</p>","PeriodicalId":15635,"journal":{"name":"Journal of Diabetes and Metabolic Disorders","volume":"24 1","pages":"89"},"PeriodicalIF":1.8,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11929648/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143700547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting COVID-19 progression in hospitalized patients in Kurdistan Province using a multi-state model.","authors":"Shnoo Bayazidi, Ghobad Moradi, Safdar Masoumi, Seyed Amin Setarehdan, Hamid Reza Baradaran","doi":"10.1007/s40200-025-01576-x","DOIUrl":"10.1007/s40200-025-01576-x","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to implement a multi-state risk prediction model to predict the progression of COVID-19 cases among hospitalized patients in Kurdistan province by analyzing hospital care data.</p><p><strong>Methods: </strong>This retrospective analysis consisted of data from 17,286 patients admitted to hospitals with COVID-19 from March 23, 2019, to December 19, 2021, in various areas in the Kurdistan province. A multi-state prediction model was used to show that each transition is predicted by a different set of variables. These variables include underlying diseases (like diabetes, hypertension, etc.) and sociodemographic information (like sex and age). Model aims to predict the likelihood of recovery, the need for critical care intervention (e.g., transfer to isolation units or the ICU), or exits from the hospitalization course. We performed the statistical analysis using R software and the mstate package.</p><p><strong>Results: </strong>Of the hospitalized patients studied, 5.6% died of the disease, 6.6% were admitted to ICUs, and 38.72% were treated in isolation units. Mortality rates in general wards, isolation units, and the ICU were 3.48%, 4.56%, and 26.6%, respectively. Significant predictors for ICU admission include age over 60 years (HR: 1.46, 95% CI 1.37-1.55), kidney-related conditions (HR: 2.19, 95% CI 1.65-2.91), cardiovascular diseases (HR: 1.68, 95% CI 1.46-1.94), lung disease (HR: 1.89,‏95% CI 1.43-2.05), and cancer (HR: 2.46,‏95% CI 1.77-3.41). The likelihood of in-hospital death is significantly increased by age over 60 years (HR: 2.40, 95% CI 2.09-2.76), diabetes (HR: 1.97, 95% CI 1.45-2.68), high blood pressure (HR: 2.30, 95% CI 1.78-2.97), and history of heart disease (HR: 3.01, 95% CI 2.29-3.95).</p><p><strong>Conclusion: </strong>The model helps the provider and policymakers to make an informed decision depending on patient management and resource allocation within the health care systems.</p>","PeriodicalId":15635,"journal":{"name":"Journal of Diabetes and Metabolic Disorders","volume":"24 1","pages":"88"},"PeriodicalIF":1.8,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11929647/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143700225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Once-weekly Basal Insulin Fc versus daily insulin degludec for glycemic control in diabetes: a systematic review, meta-analysis, and meta-regression.","authors":"Sandesh Raja, Adarsh Raja, Azzam Ali, Muhammad Sohaib Asghar","doi":"10.1007/s40200-025-01602-y","DOIUrl":"10.1007/s40200-025-01602-y","url":null,"abstract":"<p><strong>Introduction: </strong>Diabetes management often requires insulin therapy, yet adherence to daily injections can be challenging due to complexity, injection pain, and fear of hypoglycemia. Basal Insulin Fc (BIF) is a novel once-weekly insulin analog designed to simplify regimens, improve adherence, and enhance glycemic control. This meta-analysis evaluates the efficacy and safety of BIF compared to once-daily insulin degludec.</p><p><strong>Methods: </strong>A systematic search of PubMed, Google Scholar, EBSCO, ScienceDirect, and the Cochrane Library, along with ClinicalTrials.gov, was conducted up to November 2024 to identify RCTs comparing BIF with insulin degludec. The search employed MeSH terms like \"type 1 diabetes mellitus,\" \"type 2 diabetes mellitus,\" \"once weekly basal insulin Fc,\" and \"insulin degludec.\" Studies were screened in accordance with PRISMA guidelines, and data on glycemic outcomes, safety, and patient demographics were extracted. Statistical analysis included pooled mean differences (MD) and risk ratios (RR) with 95% confidence intervals (CIs) using random-effects models. Heterogeneity was assessed using the I² statistic, and sensitivity analyses were conducted for cases of high heterogeneity. Subgroup and meta-regression analyses assessed moderators such as diabetes type, insulin status, follow-up duration, and heterogeneity.</p><p><strong>Results: </strong>Five RCTs with 2,562 participants (Type 1 and Type 2 diabetes) were included. BIF showed non-inferiority to degludec in HbA1c reduction (MD 0.03, <i>p</i> = 0.37) and percentage time in range (MD 0.56, <i>p</i> = 0.27). No significant differences were observed in self-monitored fasting blood glucose (MD 2.73, <i>p</i> = 0.40) or clinically significant hypoglycemia (RR 1.00, <i>p</i> = 0.95). However, BIF increased time spent below range (MD 0.30, <i>p</i> = 0.0004) and was associated with higher treatment-emergent adverse events (RR 1.12, <i>p</i> = 0.006). The subgroup analysis highlighted differences in hypoglycemia risks between Type 1 and Type 2 diabetes.</p><p><strong>Conclusion: </strong>BIF offers comparable glycemic control to insulin degludec while reducing injection frequency, potentially enhancing adherence. However, increased hypoglycemia risks in certain subgroups and higher adverse event rates warrant further evaluation.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s40200-025-01602-y.</p>","PeriodicalId":15635,"journal":{"name":"Journal of Diabetes and Metabolic Disorders","volume":"24 1","pages":"86"},"PeriodicalIF":1.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11923323/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143691836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association between walking speed and multimorbidity patterns in community-dwelling older adults with diabetes and/or hypertension: a latent class analysis.","authors":"Janelle Gravesande, Jinhui Ma, Lauren E Griffith, Ada Tang, Julie Richardson","doi":"10.1007/s40200-025-01598-5","DOIUrl":"10.1007/s40200-025-01598-5","url":null,"abstract":"<p><strong>Purpose: </strong>Diabetes (DM) plus hypertension (HTN) is a prevalent multimorbidity pattern. However, it is unclear which other diseases frequently coexist with DM and HTN and their impact on walking speed. Therefore, we identified multimorbidity patterns in community-dwelling older adults with: i) DM, ii) HTN and iii) DM + HTN and we examined the association between multimorbidity patterns and walking speed.</p><p><strong>Methods: </strong>This was a cross-sectional study. We included 5090 community-dwelling older adults, from the National Health and Aging Trends Study, a population-based study of older adults (≥ 65 years) in the U.S. We performed latent class analysis to identify multimorbidity patterns and then performed ANCOVA to examine the association between these multimorbidity patterns and walking speed.</p><p><strong>Results: </strong>We identified 10 unique multimorbidity patterns: low multimorbidity, joint multimorbidity, cardiovascular-joint multimorbidity, psychological-joint multimorbidity, cardiovascular multimorbidity, cardiovascular-joint-respiratory multimorbidity, Metabolic-bone-joint multimorbidity, metabolic-cardiovascular-joint multimorbidity, metabolic-psychological-joint multimorbidity, metabolic-cardiovascular-joint-respiratory multimorbidity and metabolic-joint multimorbidity. Multimorbidity patterns with larger numbers of diseases and those that included psychological conditions (depression or anxiety) were associated with slower walking speeds compared to multimorbidity patterns with somatic conditions alone (e.g., arthritis).</p><p><strong>Conclusions: </strong>At a population level, these multimorbidity patterns may help to identify subgroups of older adults with slower walking speed who may benefit from targeted assessment and management to improve their walking speed.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s40200-025-01598-5.</p>","PeriodicalId":15635,"journal":{"name":"Journal of Diabetes and Metabolic Disorders","volume":"24 1","pages":"87"},"PeriodicalIF":1.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11925837/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143692389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determinants and predictors of early re-admission of patients with hyperglycemic crises: a machine learning-based analysis.","authors":"Olubola Titilope Adegbosin, Michael Adeyemi Olamoyegun, Sunday Olakunle Olarewaju","doi":"10.1007/s40200-025-01586-9","DOIUrl":"10.1007/s40200-025-01586-9","url":null,"abstract":"<p><strong>Objectives: </strong>The predictors of early re-admission of patients with diabetes mellitus (DM) have been studied with classical statistical techniques. Considering the increasing application of artificial intelligence to drive advances in medicine, this study aimed to leverage machine learning techniques to identify patients at risk of early re-admission after being admitted for hyperglycemic crises.</p><p><strong>Methods: </strong>We extracted relevant data from a publicly available dataset of patients with DM who were admitted in U.S. hospitals from 1999 to 2008. The target variable was re-admission within 30 days. Point-biserial and chi-square tests were used to assess correlations between the input and target variables. Three machine learning models were initially deployed; the model with the best recall for the positive class was selected.</p><p><strong>Results: </strong>The prevalence of early re-admission among the patients was 13.32%. Statistical tests revealed weak correlations between early re-admission and race, sex, age, use of antidiabetic medication, and numbers of non-laboratory procedures, medications, diagnoses, and visits to the emergency and inpatient departments in the previous year (all <i>p</i> < 0.05). Extreme gradient boosting classifier predicted early-re-admission with 79% recall for the positive class. The area under the receiver-operating characteristic curve was 0.78. Age and numbers of medications, emergency and inpatient visits in the previous year, and non-laboratory procedures, were the most important features for the model's prediction.</p><p><strong>Conclusions: </strong>Our findings highlight the usefulness of machine learning in making clinical decisions in the management of patients with diabetes, especially when classical statistical methods do not yield much significant information.</p>","PeriodicalId":15635,"journal":{"name":"Journal of Diabetes and Metabolic Disorders","volume":"24 1","pages":"85"},"PeriodicalIF":1.8,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11920539/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phase angle as an independent predictor of sarcopenia and glycemic control in older adults with type 2 diabetes: a cross-sectional observational study.","authors":"Go Owari, Kenichi Kono, Takahiro Nonaka, Yuto Watabe, Yusuke Nishida, Minoru Takemoto, Wataru Kakuda","doi":"10.1007/s40200-025-01590-z","DOIUrl":"10.1007/s40200-025-01590-z","url":null,"abstract":"<p><strong>Objectives: </strong>The global rise in type 2 diabetes mellitus (T2DM) poses challenges, particularly with the increasing burden of sarcopenia and poor glycemic control. Phase angle (PhA) is a promising biomarker for early detection and management of these conditions. This study aimed to evaluate PhA as an independent predictor of sarcopenia and glycemic control.</p><p><strong>Methods: </strong>This cross-sectional study included older adults with T2DM hospitalized for diabetes education between April 2021 and March 2023. Measurements included PhA, muscle mass, body fat mass, grip strength, knee extension strength, physical function (Short Physical Performance Battery and 6-min walk distance), and glycemic control (fasting blood glucose and hemoglobin A1c [HbA1c]). Sarcopenia was defined as low muscle mass and physical function. Analyses included Pearson correlations, receiver operating characteristic curve analysis, and multivariate logistic regression.</p><p><strong>Results: </strong>PhA was moderately correlated with muscle mass (<i>r</i> = 0.42, <i>p</i> < 0.001), grip strength (<i>r</i> = 0.43, <i>p</i> < 0.001), and body mass index (<i>r</i> = 0.27, <i>p</i> = 0.001), and inversely correlated with HbA1c (<i>r</i> = - 0.34, <i>p</i> < 0.001) and age (<i>r</i> = - 0.26, <i>p</i> = 0.003). PhA showed a strong predictive ability for sarcopenia (AUC = 0.83, 95% CI: 0.76-0.90, <i>p</i> < 0.001). Logistic regression indicated PhA as an independent predictor of sarcopenia (OR = 0.105, 95% CI: 0.031-0.353, <i>p</i> < 0.001) and glycemic control (OR = 0.380, 95% CI: 0.201-0.719, <i>p</i> = 0.003).</p><p><strong>Conclusions: </strong>PhA is a non-invasive, practical tool for predicting sarcopenia and monitoring glycemic control. Routine integration of PhA could identify high-risk patients and guide interventions. Future research should validate its application in diverse settings.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s40200-025-01590-z.</p>","PeriodicalId":15635,"journal":{"name":"Journal of Diabetes and Metabolic Disorders","volume":"24 1","pages":"82"},"PeriodicalIF":1.8,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11909329/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143649123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of gut microbiota in the elderly with sarcopenic obesity: a case-control study.","authors":"Fatemeh-Sadat Hosseini, Ava Behrouzi, Ebrahim Shafaie, Farshad Sharifi, Hanieh-Sadat Ejtahed","doi":"10.1007/s40200-025-01584-x","DOIUrl":"10.1007/s40200-025-01584-x","url":null,"abstract":"<p><strong>Objectives: </strong>Sarcopenic obesity is a multifactorial disorder commonly found in elderly individuals. One contributing factor is gut microbiota dysbiosis. This study compared the abundance of certain bacteria in elderly individuals with obesity and sarcopenic obesity.</p><p><strong>Methods: </strong>The study included 50 elderly individuals over 65 with a body mass index (BMI) of over 30 kg/m², both sexes. Participants were divided into two groups, each with 25 individuals, based on the diagnosis of sarcopenia using the EWGSOP2 criteria. Individuals with underlying diseases, those using antibiotics, and those with a history of gastrointestinal surgery were excluded. Stool samples were stored at -80 °C, and DNA was extracted using standard kits. Bacterial DNA sample quality was assessed using a Nanodrop device. Bacterial frequency was measured using qPCR. The log cfu for each bacteria was calculated and compared in both groups using an independent t-test. Spearman measured the correlation between bacterial genera and physical performance in SPSS 26.</p><p><strong>Results: </strong>The case group had a significantly higher average age (70.96) than the control group (68.32). The average BMI was the same in both groups. The frequency of <i>Escherichia</i> (p-value = 0.046) and <i>Bifidobacterium</i> (p-value = 0.017) was significantly higher in the case group. There was no significant difference in the frequency of <i>Lactobacillus</i> and <i>Akkermansia</i>.</p><p><strong>Conclusion: </strong>The study uncovered substantial differences in gut microbiota composition between elderly individuals experiencing sarcopenic obesity and those with obesity alone. The findings suggest that dysbiosis, characterized by an excessive presence of <i>Bifidobacterium</i>, <i>Escherichi</i>a, and <i>Akkermansia</i>, may be associated with sarcopenic obesity.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s40200-025-01584-x.</p>","PeriodicalId":15635,"journal":{"name":"Journal of Diabetes and Metabolic Disorders","volume":"24 1","pages":"83"},"PeriodicalIF":1.8,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11909374/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143649077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cardiovascular risk patterns through AI-enhanced clustering of longitudinal health data.","authors":"Maryam Mahdavi, Anoshirvan Kazemnejad, Abbas Asosheh, Davood Khalili","doi":"10.1007/s40200-025-01580-1","DOIUrl":"10.1007/s40200-025-01580-1","url":null,"abstract":"<p><strong>Objectives: </strong>A major cause of death worldwide, cardiovascular disease (CVD) is largely caused by risk factors like smoking, high blood pressure, poor diets, and a lack of physical activity. To find clear trends in the dynamics of CVD risk over time, this study used an unsupervised learning approach to examine the relationship between the incidence of CVD in Iranian adults and the longitudinal trajectories of risk factors.</p><p><strong>Methods: </strong>A total of 1872 adults aged 40-79 years, free of atherosclerotic cardiovascular disease (ASCVD) at baseline, were included in the Tehran Lipid and Glucose Study (TLGS). Longitudinal data spanning over 10 years were analyzed using clustering techniques to identify distinct trajectories of CVD risk factors. K-means clustering was applied after standardizing data using the TimeSeriesScalerMeanVariance method, and the optimal number of clusters was determined using silhouette scores.</p><p><strong>Results: </strong>The risk factor trajectories were grouped into four different clusters. Compared to Cluster 4, which represents the low-risk group, Cluster 1, which represents the high-risk group, exhibited a significantly higher hazard of CVD events. The high-risk cluster showed a noteworthy 89% incidence of CVD during the first five years of follow-up. The results suggest that risk factor trajectories may better discriminate individuals at risk of CVD.</p><p><strong>Conclusions: </strong>This study highlights the utility of trajectory-based clustering to identify high-risk individuals for CVD more effectively. Regular monitoring and longitudinal assessment of risk factor trajectories may improve the early identification of at-risk individuals and enable targeted prevention strategies to mitigate CVD incidence.</p>","PeriodicalId":15635,"journal":{"name":"Journal of Diabetes and Metabolic Disorders","volume":"24 1","pages":"84"},"PeriodicalIF":1.8,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11909301/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143649105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Associations between adherence to plant-based diets and osteoporosis and visceral fat area in middle-aged adults: evidence of a large population-based study.","authors":"Davood Soleimani, Ali Azizi, Mitra Darbandi, Maryam Sharifi, Farid Najafi, Bita Anvari, Yahya Pasdar, Mahsa Miryan","doi":"10.1007/s40200-025-01601-z","DOIUrl":"10.1007/s40200-025-01601-z","url":null,"abstract":"<p><strong>Objectives: </strong>Although plant-based diets (PBDs) are widely recognized for their cardiovascular benefits, their results on bone remain controversial. This study aimed to assess the association of PBDs with osteoporosis and fat indices in middle-aged adults.</p><p><strong>Methods: </strong>This analysis included 9,295 adults from the Ravanser Non-Communicable Disease (RaNCD) cohort. Nutritional information was collected through a validated food frequency questionnaire (FFQ), which was used to derive overall, healthy, and unhealthy PBD indices. Participants underwent the bioelectrical impedance analysis (BIA) to measure body fat (BF), fat mass index (FMI), and visceral fat area (VFA).</p><p><strong>Results: </strong>The highest tertile of healthy PBD was not associated with the odds of osteoporosis than the lowest tertile (OR for men: 1.07; 95%CI: 0.66-1.74 & OR for women: 1.24; 95%CI: 0.79-1.94). However, it was associated with a lower VFA (6.01 cm² for men and 13.64 cm² for women) than the lowest tertile. The highest tertile of overall and unhealthy PBDs was not associated with the odds of osteoporosis in men and women, while they were associated with a higher VFA [(3.22 cm² for men and 4.80 cm² for women) & (3.22 cm² for men and 11.78 cm² for women)] than the lowest tertile, respectively. A significant association was between PBD indices and BF and FMI in both sexes.</p><p><strong>Conclusions: </strong>These findings suggest that while only healthy PBDs may contribute to improved fat distribution, they do not appear to influence osteoporosis risk. Longitudinal studies are needed to explore the long-term outcome of adherence to PBDs on bone.</p>","PeriodicalId":15635,"journal":{"name":"Journal of Diabetes and Metabolic Disorders","volume":"24 1","pages":"81"},"PeriodicalIF":1.8,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11909368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143649083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}